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Percentile: Overview and How to Calculate It

Percentile: Overview and How to Calculate It

Have you ever thought about what it means to score in the 90th percentile on a standardized test or how percentiles are used to assess student performance? In this blog, we will look into what a percentile is, its types, and calculations, along with some examples and top applications. 

Table of Contents:

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What is Percentile in Statistics?

A percentile is a statistical measure indicating the relative position of a particular data point within a dataset. I can indicate what percentage of data is above or below the reference point. For example, the 90 percentile in an exam means you are above 90% of the exam takers but 10%    are above you. In simpler terms, a percentile provides insight into how a particular data point compares to the rest of the data.

Percentiles are commonly used in various fields, including education, healthcare, and finance, to assess and compare individual performances or values within a larger population.

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Types of Percentile

Percentiles are a way of dividing a set of data into equal groups based on the percentage of values that fall below a certain value. For example, if you have a set of test scores and you calculate the 25th percentile, this means that 25% of the scores in the set are lower than this value, and 75% of the scores are higher. Percentiles are a useful way of comparing performance, as they allow you to see how well someone is doing relative to others in the same group.

Here are some of the most common types of percentiles:

Quartile: Quartiles are a type of percentile that divides a set of data into four equal groups. The initial quartile (Q1) represents the point at which 25% of the data lies below it. The second quartile (Q2) corresponds to the median, marking the threshold below which 50% of the data is situated. The third quartile (Q3) designates the value beneath which 75% of the data is positioned. The interquartile range (IQR) is the difference between Q3 and Q1.

Decile: Deciles are a type of percentile that divides a set of data into ten equal groups. The first decile (D1) is the value below which 10% of the data falls. The second decile (D2) is the value below which 20% of the data falls, and so on. The ninth decile (D9) is the value below which 90% of the data falls.

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How Can You Calculate Percentiles?

Calculating percentiles involves several steps, and the specific method may vary slightly depending on the context and the software or tool you are using. Here is a general guide for calculating percentiles:

 How Can You Calculate Percentile?

Step 1: Order the Data

Arrange the dataset in ascending order, from the smallest to the largest value.

Step 2: Identify the Position

Determine the position of the percentile in the ordered dataset using the formula:

Position = (P/100) * (N+1)

where P is the percentile (e.g., 25 for the 25th percentile), and N is the total number of data points.

Step 3: Calculate the Percentile Value

Once you have the position, locate the corresponding value in the ordered dataset. If there’s an interpolation, use the weighted average of the two closest values.


Let’s say you have the following dataset: 15, 25, 65, 35, 20, 55, 45, 30, 40, 60, 50, and you want to find the 25th percentile (Q1).

1. Order the Data:

15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65.

2. Identify the Position:

Position =(25/100) * (11+1)

3. Calculate the Percentile Value:

The 25th percentile is the value at the 3rd position in the ordered dataset, which is 25.

So, the 25th percentile (Q1) for this dataset is 25.

Percentage vs. Percentile

The main difference between percentage and percentile is that percentage is a measure of relative proportion out of 100, while percentile is a measure of relative position within a dataset. Percentages are often used in everyday contexts, while percentiles are more common in statistical analysis.

DefinitionA percentage is a ratio expressed as a fraction of 100.A percentile is a measure indicating the relative position of a data point within a dataset.
RepresentationRepresented by the symbol %.Represented by the symbol “th”. Example: 25th percentile.
Calculation FormulaPercentage = (Part/Whole)*100Percentiles are calculated based on the position of a data point in an ordered dataset.
RangeRanges from 0% to 100%.Ranges from 0 to 100, indicating the position of a data point in a dataset.
ExamplesIf you score 80 out of 100, your percentage is 80/100*100 = 80%If you are in the 75th percentile in a test, it means you scored higher than 75% of the participants.
Use CasesCommonly used in grading systems, finance, and expressing proportions.Used in statistics to describe the relative standing of a data point in a distribution.
Independent of ScaleDepends on the scale of the whole.Independent of the scale of the dataset; it reflects the position of a value within the dataset.
Whole and Part FocusFocuses on the relationship between a part and a whole.Focuses on the position of a specific value in a dataset relative to other values.
ApplicabilityApplicable in a variety of contexts, especially when dealing with proportions or ratios.Primarily used in statistical analysis to describe the distribution of data.

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Examples of Percentile

Problem Statement: The marks obtained by students are 25, 30, 35, 42, 50, 55, 60, 68, 75, and 80. What is the percentile for a score of 90 using the percentile formula?

Solution: Let’s calculate the percentile for the score of 90 using the given data.

Given Data:

Scores: 25, 30, 35, 42, 50, 55, 60, 68, 75, 80

Order the Data:


Identify the Position:

Use the formula to find the position of the score 90

Position = (Number of values below 90 / Total number of values) * 100

-> Position = (10 / 10) * 100 = 100


The 90th percentile for this dataset is 100.


The 90th percentile score of 100 indicates that all of the students scored below or equal to 100, which is the maximum score in this dataset.

How Percentile is Calculated in CAT?

The Common Admission Test (CAT) percentile is calculated based on the relative performance of all CAT test-takers. It displays the percentage of applicants who performed worse on the test than a specific applicant. A better performance is indicated by a higher percentile.

Here’s a step-by-step explanation of how the CAT percentile is calculated:

Raw Score Calculation: CAT scores are first normalized to account for variations in difficulty levels across different CAT exams. This normalization process results in scaled scores for each section (verbal ability, reading comprehension, data interpretation and logical reasoning, and quantitative ability).

Rank Determination: Based on the scaled scores, candidates are assigned ranks within each section and for the overall exam. The rank represents the position of a candidate relative to other test-takers.

Percentile Calculation: The percentile for each candidate is calculated using the following formula:

Percentile = (1 – (Rank / Total Number of Candidates)) * 100

For example if a candidate secures a rank of 1000 out of 200,000 CAT test-takers, their percentile would be:

Percentile = (1 – (1000 / 200,000)) * 100 = 99.5%

This means that the candidate has scored better than 99.5% of all CAT test-takers.

If You Have 100 Percentile in CAT, What Does it Mean?

A 100 percentile means that you are the top scorer of the exam. Two people can have the same percentile if they have the same scores. 

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Top Applications of Percentile in Statistics 

Percentiles are widely used in statistics for various applications. Here are some of the top applications of percentiles:

  • Education and Testing
    In academic assessments, percentiles are used to evaluate and compare the performance of students. For example, standardized test scores often come with percentile ranks, indicating how a student’s performance compares to that of a reference group.
  • Healthcare and Growth Charts
    In pediatric medicine, percentiles are frequently used in growth charts to assess the physical development of children. Pediatricians use percentiles to evaluate a child’s height, weight, and other physical parameters in comparison to a standardized population.
  • Economic and Financial Analysis
    In finance, percentiles are employed to analyze income distributions, wealth disparities, and market performance. Percentile income rankings, for example, help understand the income distribution across different segments of the population.
  • Demographic Studies
    In demographic research, percentiles are used to analyze various population characteristics, such as income, education level, or age distribution. This information aids in understanding the socioeconomic makeup of different groups.
  • Employee Performance Evaluation
    In human resources, percentiles are used to evaluate and rank employee performance. This can be applied to performance reviews, salary comparisons, or other metrics to assess how an employee’s performance compares to their peers.
  • Data Analysis and Outlier Detection
    Percentiles are useful for outlier detection, helping identify extreme values that might skew data analysis and decision-making. By analyzing the distribution of data points, percentiles can highlight unusual values that may warrant further investigation or removal from the analysis.
  • Time Series Analysis:
    In time series analysis, percentiles can be used to identify trends and patterns in data over time. This is common in fields like climate science, where percentiles help identify extreme weather events or long-term trends.


Percentiles serve as a crucial statistical tool with diverse applications across fields. Whether assessing academic performance, understanding economic disparities, or evaluating healthcare metrics, percentiles offer a standardized method to measure relative standing within a dataset. By providing a comparative measure, percentiles enable informed decision-making, highlight distribution patterns, and contribute to hidden analyses in education, finance, healthcare, and beyond. Their adaptability makes percentiles an indispensable tool, bridging the gap between raw data and meaningful insights in our complex, data-driven world.

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What is the difference between percentage and percentile

Percentage is a measure of how much out of the total while percentile is a measure of relative position out of the total.

On what parameters percentile depend?

The most important parameters on which a percentile depends are the ranking method and data Size.

Can the percentile of two data points be the same?

Yes, if the data points are the same, the percentiles representing them can be the same based on the ranking method.

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